NCT06282068

AI Determine Malignancy of GGO on Chest CT

Status unknown Last updated 28 February 2024

What this trial tests

trial testing AI computer-aided detection software in Lung Nodules, Early Lung Cancer, Artificial Intelligence, Chest CT, Minimally Invasive Surgery, Lung Image Analysis Software in 100 participants. Status unknown.

Timeline

1 March 2024

Primary endpoint
28 February 2026

28 February 2026

Quick facts

Lead sponsor	Chung Shan Medical University
Status	Status unknown
Study type	OBSERVATIONAL
Enrollment	100
Start date	1 March 2024
Primary completion	28 February 2026
Estimated completion	28 February 2026
Sites	1 location across Taiwan

Drugs / interventions tested

• AI computer-aided detection software

Conditions studied

• Lung Nodules, Early Lung Cancer, Artificial Intelligence, Chest CT, Minimally Invasive Surgery, Lung Image Analysis Software — all drugs for Lung Nodules, Early Lung Cancer, Artificial Intelligence, Chest CT, Minimally Invasive Surgery, Lung Image Analysis Software →

Sponsor

Chung Shan Medical University

Who can join

20 and older, any sex, with Lung Nodules, Early Lung Cancer, Artificial Intelligence, Chest CT, Minimally Invasive Surgery, Lung Image Analysis Software. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

Research Objectives To use AI computer-aided detection software to assist physicians in reading CT scans of lung nodules, providing auxiliary diagnostic tools for medical decision-making. The software can mark nodule locations and related information during routine physician reading. This study will obtain prospective consent to use patient CT images for software reading and compare with clinical physician diagnosis, in order to enhance software training and improve recognition of lung lesions for early diagnosis and treatment.

Study Design Collect CT images of untreated lung nodules 4-30mm in size that are scheduled for surgery. No limits on age, gender, disease type, with image resolution \<2.5mm. AI and clinicians will judge nodule characteristics separately. Surgical resection followed by comparison with pathology reports will evaluate diagnostic accuracy.

Study Procedures A double-blinded method will be used. AI and physicians will record nodules as likely benign or malignant separately. After surgical resection, the lesions will undergo pathological staging and the diagnostic accuracy of both groups will be compared.

Expected Results Compare the diagnostic accuracy of AI and clinicians to improve AI training quality, achieve early diagnosis and treatment goals, and provide patients with better medical care quality.

Monitoring Method AI and clinicians will read separately, adhering to shared decision making without affecting patient access to diagnosis and treatment.

Keywords: lung nodules, early lung cancer, artificial intelligence, chest CT, minimally invasive surgery, lung image analysis software

Publications & conference data

1 peer-reviewed publication reference this trial (live from Europe PMC):

1. Minimally invasive biomarkers for triaging lung nodules-challenges and future perspectives.
   Afridi WA, Picos SH, Bark JM, Stamoudis DAF, et al · · 2025 · cited 5× · PMID 39888565 · DOI 10.1007/s10555-025-10247-5